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Eliciting Users' Attitudes toward Smart Devices

Authors:

Ingrid Zukerman,

Masud Moshtaghi,

Gwyneth ReesAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 175 - 184

https://doi.org/10.1145/2930238.2930241

Published: 13 July 2016 Publication History

Abstract

This paper presents a study to determine users' attitudes toward smart devices. We conducted a web survey to elicit users' ratings for devices and combinations of tasks and devices; the results of this survey led to the development of a Recommender System (RS) for smart devices for particular tasks. We investigated user- and item-based Collaborative Filters, and compared their performance with that of global and demographic RS baselines. We then developed a technique based on Principal Components Analysis to select a subset of the original survey questions that supports the prediction of users' ratings for device-task combinations. Our results show that the accuracy of an RS that asks only a small subset of the survey questions is similar to that of an RS that predicts users' answers to one survey question on the basis of their answers to all the other questions.

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Cited By

Wu DDong JYuan FCheng L(2022)Users’ search performance prediction in cross-device searchJournal of Librarianship and Information Science10.1177/0961000622109095655:2(464-477)Online publication date: 27-Apr-2022
https://doi.org/10.1177/09610006221090956
Wu DLiang S(2018)Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and DevicesSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00831ED1V01Y201802ICR06310:2(i-159)Online publication date: 19-Mar-2018
https://doi.org/10.2200/S00831ED1V01Y201802ICR063
Zhan KZukerman IPartovi A(2018)Identifying factors that influence the acceptability of smart devicesUser Modeling and User-Adapted Interaction10.1007/s11257-018-9210-028:4-5(391-423)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11257-018-9210-0
Show More Cited By

Index Terms

Eliciting Users' Attitudes toward Smart Devices
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models

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Information & Contributors

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Published In

cover image ACM Conferences

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2016

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Air Force Office of Scientific Research Asian Office of Aerospace Re- search and Development (AOARD)

Conference

UMAP '16

Sponsor:

UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

Acceptance Rates

UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

Wu DDong JYuan FCheng L(2022)Users’ search performance prediction in cross-device searchJournal of Librarianship and Information Science10.1177/0961000622109095655:2(464-477)Online publication date: 27-Apr-2022
https://doi.org/10.1177/09610006221090956
Wu DLiang S(2018)Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and DevicesSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00831ED1V01Y201802ICR06310:2(i-159)Online publication date: 19-Mar-2018
https://doi.org/10.2200/S00831ED1V01Y201802ICR063
Zhan KZukerman IPartovi A(2018)Identifying factors that influence the acceptability of smart devicesUser Modeling and User-Adapted Interaction10.1007/s11257-018-9210-028:4-5(391-423)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11257-018-9210-0
Kertész CTurunen M(2018)Exploratory analysis of Sony AIBO usersAI & SOCIETY10.1007/s00146-018-0818-8Online publication date: 3-Feb-2018
https://doi.org/10.1007/s00146-018-0818-8
Wu DCheng L(2018)Predicting Search Performance from Mobile Touch Interactions on Cross-device Search Engine Result PagesTransforming Digital Worlds10.1007/978-3-319-78105-1_62(560-570)Online publication date: 15-Mar-2018
https://doi.org/10.1007/978-3-319-78105-1_62

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